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This paper introduces the Belief-Aware Influence and Trust (BAIT) controller, which addresses the challenges of repeated human-robot interaction by integrating a hierarchical particle filter to track human belief updates and a Model Predictive Path Integral planner to optimize the balance between influence and trust. The approach ensures immediate task performance while maintaining user trust, contrasting with traditional methods that often lead to performance decay due to erratic robot behavior. Experimental results from simulations, human-subject studies, and real-world deployments demonstrate that BAIT achieves comparable task performance to existing methods while significantly enhancing user trust.
BAIT not only maintains task performance but also boosts user trust, outperforming traditional methods that sacrifice reliability for influence.
Repeated human-robot interaction (HRI) requires proactively accounting for humans who continually adapt to evolving beliefs about the robot. Prior frameworks often treat encounters as isolated events, suffering cumulative task performance decay as human perception drifts, or maintain long-term influence through erratic, unpredictable behavior that erodes perceived human trust and relies on computationally unscalable formulations. To address these gaps, we introduce the Belief- Aware Influence and Trust (BAIT) controller. BAIT integrates a hierarchical particle filter, which infers both fast human strategic shifts and slow perceptual belief updates, with a belief-aware Model Predictive Path Integral planner. BAIT explicitly optimizes the trade-off between long-horizon influence and human trust, while enforcing immediate task performance as a strict constraint. Across simulations, a human-subject study, and a real-world GEM vehicle deployments in repeated lane-merging scenarios, BAIT achieves task performance comparable to baselines that optimize long-term influence through unpredictability while yielding significantly higher user trust. The video demonstrating our experiments is available at https://youtu.be/GsPfHRujzVs.